Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP model
IntroductionExisting facility environment prediction models often suffer from low accuracy, poor timeliness, and error accumulation in long-term predictions under multifactor nonlinear coupling conditions. These limitations significantly constrain the effectiveness of precise environmental regulatio...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1652478/full |
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| author | Longwei Liang Longwei Liang Hui Shi Hui Shi Zhaoyuan Wang Shengjie Wang Changhong Li Ming Diao |
| author_facet | Longwei Liang Longwei Liang Hui Shi Hui Shi Zhaoyuan Wang Shengjie Wang Changhong Li Ming Diao |
| author_sort | Longwei Liang |
| collection | DOAJ |
| description | IntroductionExisting facility environment prediction models often suffer from low accuracy, poor timeliness, and error accumulation in long-term predictions under multifactor nonlinear coupling conditions. These limitations significantly constrain the effectiveness of precise environmental regulation in agricultural facilities.MethodsTo address these challenges, this paper proposes a novel facility environment prediction model (LSTM-AT-DP) integrating Long Short-Term Memory networks with attention mechanisms and advanced data preprocessing. The model architecture employs: (1) a Data Preprocessing (DP) module combining Wavelet Threshold Denoising (WTD) for noise elimination and Sliding Window (SW) technique for feature matrix construction; (2) an LSTM core for deep temporal modeling; and (3) an Attention Mechanism (AT) for dynamic feature weighting to enhance critical temporal feature extraction.ResultsIn 24-hour prediction tests, the model achieved determination coefficients (R²) of 0.9602 (temperature), 0.9529 (humidity), and 0.9839 (radiation), representing improvements of 3.89%, 5.53%, and 2.84% respectively over baseline LSTM models. Corresponding RMSE reductions were 0.6830, 1.8759, and 12.952 for these parameters.DiscussionThe results demonstrate that the LSTM-AT-DP model significantly enhances prediction accuracy while effectively suppressing error accumulation in long-term forecasts. This advancement provides robust technical support for precise facility environment regulation, with particular improvements observed in humidity prediction. The integrated attention mechanism proves particularly effective in identifying and weighting critical temporal features across all measured environmental parameters. |
| format | Article |
| id | doaj-art-7f818249a31a4e28a4e80aa84e5a9348 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-7f818249a31a4e28a4e80aa84e5a93482025-08-20T03:46:46ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-08-011610.3389/fpls.2025.16524781652478Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP modelLongwei Liang0Longwei Liang1Hui Shi2Hui Shi3Zhaoyuan Wang4Shengjie Wang5Changhong Li6Ming Diao7College of Agriculture, Shihezi University, Shihezi, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, ChinaInternational PhD School, University of Almería, Almería, SpainCollege of Agriculture, Shihezi University, Shihezi, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi, ChinaCollege of Agriculture, Shihezi University, Shihezi, ChinaCollege of Agriculture, Shihezi University, Shihezi, ChinaIntroductionExisting facility environment prediction models often suffer from low accuracy, poor timeliness, and error accumulation in long-term predictions under multifactor nonlinear coupling conditions. These limitations significantly constrain the effectiveness of precise environmental regulation in agricultural facilities.MethodsTo address these challenges, this paper proposes a novel facility environment prediction model (LSTM-AT-DP) integrating Long Short-Term Memory networks with attention mechanisms and advanced data preprocessing. The model architecture employs: (1) a Data Preprocessing (DP) module combining Wavelet Threshold Denoising (WTD) for noise elimination and Sliding Window (SW) technique for feature matrix construction; (2) an LSTM core for deep temporal modeling; and (3) an Attention Mechanism (AT) for dynamic feature weighting to enhance critical temporal feature extraction.ResultsIn 24-hour prediction tests, the model achieved determination coefficients (R²) of 0.9602 (temperature), 0.9529 (humidity), and 0.9839 (radiation), representing improvements of 3.89%, 5.53%, and 2.84% respectively over baseline LSTM models. Corresponding RMSE reductions were 0.6830, 1.8759, and 12.952 for these parameters.DiscussionThe results demonstrate that the LSTM-AT-DP model significantly enhances prediction accuracy while effectively suppressing error accumulation in long-term forecasts. This advancement provides robust technical support for precise facility environment regulation, with particular improvements observed in humidity prediction. The integrated attention mechanism proves particularly effective in identifying and weighting critical temporal features across all measured environmental parameters.https://www.frontiersin.org/articles/10.3389/fpls.2025.1652478/fullLSTMattention mechanismwavelet threshold denoisingmulti-factor time series forecastingenvironmental prediction |
| spellingShingle | Longwei Liang Longwei Liang Hui Shi Hui Shi Zhaoyuan Wang Shengjie Wang Changhong Li Ming Diao Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP model Frontiers in Plant Science LSTM attention mechanism wavelet threshold denoising multi-factor time series forecasting environmental prediction |
| title | Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP model |
| title_full | Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP model |
| title_fullStr | Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP model |
| title_full_unstemmed | Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP model |
| title_short | Research on time series prediction model for multi-factor environmental parameters in facilities based on LSTM-AT-DP model |
| title_sort | research on time series prediction model for multi factor environmental parameters in facilities based on lstm at dp model |
| topic | LSTM attention mechanism wavelet threshold denoising multi-factor time series forecasting environmental prediction |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1652478/full |
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